Quantum inspired algorithm for microcalcification detection in mammograms

This paper introduces a novel methodology based on quantum signal processing (QSP) and cellular automata (CA) to detect microcalcifications in mammograms. The methodology includes algorithms to detect microcalcification in binary and grayscale images. Essentially, quantum algorithms are used to improve edge detection results and CA are used to enhance the definition of the microcalcification in both groups of images. In grayscale images, CA are used to identify areas having a high probability of being microcalcifications. Benchmark images from the Mammographic Image Analysis Society (Mini-MIAS) digital mammogram database and the Digital Database for Screening Mammography (DDSM), which include mammograms showing several abnormalities previously evaluated by experts, were used in our study. Experiments using the two quantum processing algorithms were conducted, and the results were compared with those of other approaches for microcalcification detection.